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Multiscale Embedded Gene Co-expression Network Analysis.

Song WM, Zhang B - PLoS Comput. Biol. (2015)

Bottom Line: Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases.However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness.MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches.

View Article: PubMed Central - PubMed

Affiliation: Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.

ABSTRACT
Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG) has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(/V/3), the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) by: i) introducing quality control of co-expression similarities, ii) parallelizing embedded network construction, and iii) developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA). MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma.

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The global BRCA PFN.Different node colors represent different clusters identified at a scale of α = 1.3. Node size and label size are proportional to node degree.
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pcbi.1004574.g004: The global BRCA PFN.Different node colors represent different clusters identified at a scale of α = 1.3. Node size and label size are proportional to node degree.

Mentions: Degree distributions and diameters of PFNs were computed since these are the key network topological characteristics representing the hallmark features such as scale-free and small-world networks [28]. We present the PFNs constructed from PCC in this section. Fig 4 shows the global PCC-based BRCA PFN and its clusters identified at a scale defined by α = 1.3, and S2 Fig shows the global PCC-based LUAD and its clusters at α = 1. We reserve “k” to denote the node degree only in this section, but use k to denote the number of clusters in the other sections.


Multiscale Embedded Gene Co-expression Network Analysis.

Song WM, Zhang B - PLoS Comput. Biol. (2015)

The global BRCA PFN.Different node colors represent different clusters identified at a scale of α = 1.3. Node size and label size are proportional to node degree.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4664553&req=5

pcbi.1004574.g004: The global BRCA PFN.Different node colors represent different clusters identified at a scale of α = 1.3. Node size and label size are proportional to node degree.
Mentions: Degree distributions and diameters of PFNs were computed since these are the key network topological characteristics representing the hallmark features such as scale-free and small-world networks [28]. We present the PFNs constructed from PCC in this section. Fig 4 shows the global PCC-based BRCA PFN and its clusters identified at a scale defined by α = 1.3, and S2 Fig shows the global PCC-based LUAD and its clusters at α = 1. We reserve “k” to denote the node degree only in this section, but use k to denote the number of clusters in the other sections.

Bottom Line: Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases.However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness.MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches.

View Article: PubMed Central - PubMed

Affiliation: Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.

ABSTRACT
Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG) has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(/V/3), the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) by: i) introducing quality control of co-expression similarities, ii) parallelizing embedded network construction, and iii) developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA). MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma.

Show MeSH
Related in: MedlinePlus